Data Scientist Interview Questions

Showing 481–510 of 513 results

• UberMedium·Data Scientist
  Evaluate ETA Impact on Conversion

  This question evaluates a data scientist's skills in causal inference, experimental design, and marketplace analytics, including understanding of confounding, selection bias, interference, and the distinction between correlation and causation.

  Analytics & ExperimentationTechnical Screen
• UberMedium·Data Scientist
  Evaluate Marketplace Changes

  This problem tests your ability to design an A/B test for a two-sided marketplace, focusing on network effects, metric selection, and identifying potential interference between treatment and control groups.

  Analytics & ExperimentationOnsiteExperimentationA/B TestingMarketplace
• UberMedium·Data Scientist
  Evaluate Rider-Incentive Program Impact with Key Metrics

  Uber data scientist analytics prompt on evaluating a rider-incentive program, covering experiment design, rider and driver metrics, matching quality, ROI, marketplace externalities, spillovers, and guardrails.

  Analytics & ExperimentationTechnical Screen
• UberMedium·Data Scientist
  Evaluate UberEATS priority delivery and membership

  This question tests the ability to design an experiment for a marketplace feature and evaluate its impact on key business metrics like conversion, frequency, and total contribution margin.

  Analytics & ExperimentationTechnical ScreenExperimentationMarketplaceCausal Inference
• UberMedium·Data Scientist
  Evaluate a cold-start rating launch

  This question tests the ability to design an evaluation framework for a new feature in a two-sided marketplace while accounting for spillover effects and metrics that represent different stakeholder interests.

  Analytics & ExperimentationTechnical ScreenA/B TestingMarketplace AnalyticsCausal Inference
• UberMedium·Data Scientist
  Evaluate business value of lower ETA

  This question tests the ability to design an experiment that isolates the causal impact of reduced ETA on user retention and to translate these metrics into business value.

  Analytics & ExperimentationTechnical ScreenExperimentationCausal InferenceMarketplace Analytics
• UberHard·Data Scientist
  Evaluate impact without randomized experiments

  This question evaluates a data scientist's competence in causal inference and identification strategies for estimating promotion effects from observational longitudinal data, emphasizing challenges such as staggered rollout, interference (spillovers), seasonality, and uncertainty quantification.

  Analytics & ExperimentationTechnical Screen
• UberMedium·Data Scientist
  Evaluate marketplace interventions

  This question tests the ability to design an experimentation framework for a two-sided marketplace, specifically addressing interference between supply and demand and selecting appropriate success metrics.

  Analytics & ExperimentationOnsiteExperimentationMarketplaceCausal Inference
• UberHard·Data Scientist
  Explain and validate A/B test assumptions

  This question evaluates proficiency in experimental design, causal inference, and statistical diagnostics for A/B testing, covering competencies such as randomization integrity, SUTVA/interference, noncompliance, time-varying effects, metric missingness, sequential testing, and heterogeneous treatment effects.

  Analytics & ExperimentationTechnical Screen
• UberEasy·Data Scientist
  How to experiment on ETA reduction

  This problem tests the ability to design an A/B test for product improvements and address potential confounding factors like selection bias and spillover effects.

  Analytics & ExperimentationTechnical ScreenA/B TestingCausal InferenceMarketplace Dynamics
• UberHard·Data Scientist
  Improve Estimated Time of Arrival for Uber Riders

  This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Improve Estimated Time of Arrival for Uber Riders states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationTechnical Screen
• UberHard·Data Scientist
  Investigate ride declines and test free trials

  This question evaluates proficiency in product analytics, cohort and funnel analysis, retention and supply diagnostics, pricing and subscription economics, and experimentation design.

  Analytics & ExperimentationOnsite
• UberEasy·Data Scientist
  Measure feature impact with switchback, PSM, and CACE

  This question evaluates proficiency in causal inference and experimental design for product metrics, specifically testing knowledge of switchback experiments, time-series adjustments (seasonality and autocorrelation), propensity score methods for observational comparisons, and complier-average causal effect (LATE/CACE) estimation; it belongs to the Analytics & Experimentation and Data Science domain. It is commonly asked because it probes handling of real-world complications—time and city heterogeneity, non-compliance, and derived-metric inference—and tests both conceptual understanding of causal assumptions and practical application of statistical adjustment and inference.

  Analytics & ExperimentationTechnical Screen
• UpstartEasy·Data Scientist
  Estimate impact without experiments and pick variant

  This question evaluates causal inference and experimental-analysis competencies in Analytics & Experimentation and Data Science, covering observational estimands, causal identification assumptions and biases, uncertainty quantification for A/B/C tests, multiple-comparisons reasoning, and post-launch forecasting and monitoring.

  Analytics & ExperimentationTechnical Screen
• UpstartHard·Data Scientist
  Evaluate channels and allocate budget

  This question evaluates competency in marketing analytics and experimentation for a Data Scientist role, covering funnel analysis, conversion and CAC metrics, attribution modeling, response-curve-based budget allocation, and incremental test design using channel-level spend and outcome data.

  Analytics & ExperimentationTechnical Screen
• UpstartHard·Data Scientist
  Explain Treatment Results and Recommend Launch Criteria for Experiments

  Explain Treatment Results and Recommend Launch Criteria for Experiments evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationTechnical Screen
• UpstartHard·Data Scientist
  Formulate hypotheses and metrics for video-pin ramp

  This question evaluates experiment-design and data-science competencies including hypothesis specification, metric choice and guardrails, segmentation, interference handling, rollout planning, power/MDE considerations, and data-quality monitoring, and it falls under the Analytics & Experimentation domain.

  Analytics & ExperimentationHR Screen
• UpstartMedium·Data Scientist
  How would you measure causal impact?

  Answer causal-impact and three-variant experiment prompts with clear estimands, quasi-experimental assumptions, validation, uncertainty, by-hand CTP intervals, multiple-comparison caveats, business guardrails, and Bayesian forecasting for production conversion.

  Analytics & ExperimentationTechnical Screen
• WayfairEasy·Data Scientist
  Derive insights and improve complaint resolutions

  This problem tests the ability to join disparate datasets to identify trends, evaluate the effectiveness of resolution strategies, and design an A/B test to validate potential improvements in customer satisfaction.

  Analytics & ExperimentationOnline AssessmentSQLCausal InferenceA/B Testing
• WayfairEasy·Data Scientist
  How to improve complaint resolution

  This question evaluates a candidate's competency in data-driven analytics, causal inference, metrics design, and experimentation for customer support performance.

  Analytics & ExperimentationOnline Assessment
• WaymoMedium·Data Scientist
  Conservative Wait Estimates Cut Conversion and Pickup Time: Did Efficiency Improve?

  A ride service showed riders conservative pickup-time estimates; afterwards conversion fell 5% and actual time to pickup fell 25%. Decide whether fleet efficiency really improved and list alternative explanations, testing selection effects and metric design.

  Analytics & ExperimentationOnsite
• WaymoMedium·Data Scientist
  Evaluate Smart Wait Launch Impact

  This question evaluates a data scientist's competencies in causal inference and experiment analysis, including interpreting A/B test outcomes, trade-offs between conversion and operational metrics, selection of primary and guardrail metrics, and generation of alternative hypotheses.

  Analytics & ExperimentationOnline Assessment
• WaymoMedium·Data Scientist
  Evaluate a New Version in Simulation and Explain a Flat Live Result

  Compare model versions on paired simulation scenarios and diagnose why offline improvements may disappear in live rare-event evaluation.

  Analytics & ExperimentationTechnical Screen
• WaymoEasy·Data Scientist
  How validate a driving simulation is realistic?

  This question tests the ability to design an evaluation framework to compare simulated vehicle behavior against real-world sensor and trajectory logs to ensure high-fidelity simulation.

  Analytics & ExperimentationOnsiteSimulationValidationStatistics
• WaymoMedium·Data Scientist
  Interpret a Smart Wait Launch with Conflicting Metrics

  Interpret a product launch where conservative wait estimates reduce conversion while observed pickup time also falls. Diagnose selection effects and alternative causes, define customer and fleet metrics with consistent denominators, and propose an experiment that can separate display accuracy from operational improvement.

  Analytics & ExperimentationOnline Assessment
• WaymoMedium·Data Scientist
  Test Whether a Routing Experiment Reduced Pickup Time

  Analyze a routing A/B test in Python by filtering and joining user and ride data, then calculating a Welch t-test for pickup time. Interpret effect size and uncertainty, address repeated rides and post-treatment selection, and design stronger user-level or cluster-aware inference.

  Analytics & ExperimentationOnline Assessment
• WaymoMedium·Data Scientist
  Welch's t-Test p-Value for a Routing Experiment on Time to Pickup, and Its Validity

  Filter rides to one city and month, join users to their experiment variants, and compute a Welch's t-test p-value on time to pickup in Python. Then judge whether the routing test supports a conclusion and design a better experiment for a shared fleet.

  Analytics & ExperimentationOnsite
• WAWeiss Asset ManagementMedium·Data Scientist
  Evaluate and Monitor a Paid Electricity Price Forecast

  This problem tests the ability to design an evaluation framework for time-series forecasting models and systematic troubleshooting of performance degradation in an automated trading pipeline.

  Analytics & ExperimentationTechnical ScreenTime SeriesBacktestingModel Evaluation
• Wells FargoMedium·Data Scientist
  Design Excel visuals for risk results

  Design Excel visuals for risk results evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationTechnical Screen
• WhatnotMedium·Data Scientist
  Define Marketplace Success Metrics and Investigate Their Movement

  A product analytics interview about defining success for a two-sided marketplace and diagnosing an unexpected metric movement. It tests metric decomposition, cohort and funnel analysis, instrumentation checks, and the ability to separate product effects from mix shifts.

  Analytics & ExperimentationTechnical Screen